Stephen Friend CRUK-MD Anderson Cancer Workshop 2012-02-28

Open Source pre-competitive drug discovery

Moving beyond linear investigations Both of the science and of how we work

Stephen Friend MD PhD

Sage Bionetworks (Non-Profit Organization) Seattle/ Beijing/ Amsterdam

February 28, 2012

Partnering  &  Collabora/on-­‐So  what  has  been  possible?  

         All  pa&ents  now  >25,000  at  a  Cancer  Center  partnered  provide  consented  expression  on  their  pts  for  classifying  sub-­‐popula&ons  

 Combina&on  Therapies-­‐  each  at    Ph  I-­‐  joint  development  2  Pharma  

 Sharing  all  the  CT  Onc  Trial  imagining  files  among  2  Pharma  

 Link  Parma  with  an  “Ins&tute  for  Applied  Cancer  Center”  

     Share  genomic  data    on  25,000  samples    with  clinical  records  and  Expression  and  Exomes  among  three  Pharma  

Partnering  &  Collabora/on-­‐So  what  has  been  possible?  

         All  pa&ents  now  >25,000  at  a  Cancer  Center  partnered  provide  consented  expression  on  their  pts  for  classifying  sub-­‐popula&ons  

 2006      MoffiP  Cancer  Center-­‐  Merck    

 Combina&on  Therapies-­‐  each  at    Ph  I-­‐  joint  development  2  Pharma  

 2007    AZ  Merck  (Mek/Akt)  

 Sharing  all  the  CT  Onc  Trial  imagining  files  among  2  Pharma    2008    BMS  &  Merck  

 Link  Parma  with  an  ”  Ins&tute  for  Applied  Cancer  Center”  

 2008    Belfer-­‐  Merck  

     Share  genomic  data    on  25,000  samples    with  clinical  records  and  Expression  and  Exomes  among  three  Pharma  

 2010              Asian  Cancer  Research  Group  ACRG-­‐    Lilly  Merck  Pfizer  

So  what  is  the  problem?  

     Most  approved  therapies  were  assumed  to  be  monotherapies  for  diseases  represen&ng  homogenous  popula&ons  

 Our  exis&ng  disease  models  o]en  assume  pathway  knowledge  sufficient  to  infer  correct  therapies  

Familiar but Incomplete

Reality: Overlapping Pathways

what will it take to understand disease?

                   DNA    RNA  PROTEIN  (dark  maCer)    

MOVING  BEYOND  ALTERED  COMPONENT  LISTS  

DIVERSE  POWERFUL  USE  OF  MODELS  AND  NETWORKS  

  50 network papers   http://sagebase.org/research/resources.php

List of Influential Papers in Network Modeling

(Eric Schadt)

Sage Mission

Sage Bionetworks is a non-profit organization with a vision to create a “commons” where integrative bionetworks are evolved by

contributor scientists with a shared vision to accelerate the elimination of human disease

Sagebase.org

Data Repository

Discovery Platform

Building Disease Maps

Commons Pilots

Sage Bionetworks Collaborators

  Pharma Partners   Merck, Pfizer, Takeda, Astra Zeneca, Amgen, Roche

12

  Foundations   Kauffman CHDI, Gates Foundation

  Government   NIH, LSDF, NCI

  Academic   Levy (Framingham)   Rosengren (Lund)   Krauss (CHORI)

  Federation   Ideker, Califano, Nolan, Schadt

RULES GOVERN PL

ATFO

RM

NEW

MAP

S

RULES GOVERN PL

ATFO

RM

NEW

MAP

S

Why not share clinical /genomic data and model building within teams in ways currently used by the software industry

(power of tracking workflows and versioning

Leveraging Existing Technologies

Taverna

Addama

tranSMART

Watch What I Do, Not What I Say sage bionetworks synapse project

Reduce, Reuse, Recycle sage bionetworks synapse project

Most of the People You Need to Work with Don’t Work with You

sage bionetworks synapse project

My Other Computer is Cloudera Amazon Google

sage bionetworks synapse project

Sage Metagenomics Project

•  > 10k genomic and expression standardized datasets indexed in SCR •  Error detection, normalization in mG •  Access raw or processed data via download or API in downstream analysis •  Building towards open, continuous community curation

Processed Data (S3)

Sage Metagenomics using Amazon Simple Workflow

Full case study at http://aws.amazon.com/swf/testimonials/swfsagebio/

Amazon SWF and Synapse

•  Maintains state of analysis •  Tracks step execution •  Logs workflow history •  Dispatches work to Amazon or

remote worker nodes •  Efficiently match job size to

hardware •  Provides error handling and

recovery

•  Hosts raw and processed data for further reuse in public or private projects

•  Provides visibility into intermediate results and algorithmic details

•  Allows programmatic access to data; integration with R

•  Provides standard terminologies for annotations

•  Search across data sets

Synapse Roadmap

Q1-2012 Q2-2012 Q3-2012 Q4-2012 Q1-2013 Q2-2013

Synapse Platform Functionality

Data / Analysis Capabilities

Q3-2013 Q4-2013

Internal Alpha Public Beta Testing Synapse 1.0 Synapse 1.5 Future

•  Data Repository •  Projects and security •  R integration •  Analysis provenance

• Search • Controlled Vocabularies • Governance of restricted data

•  40+ manually curated clinical studies •  8000 + GEO / Array Express datasets •  Clinical, genomic, compound sensitivity •  Bioconductor and custom R analysis

• TCGA •  METABRIC breast cancer challenge

•  Workflow templates •  Publishing figures •  Wiki & collaboration tools •  Integrated management of cloud resources

•  Social networking •  User-customized dashboards •  R Studio integration •  Curation tool integration

•  Predictive modeling workflows •  Automated processing of common genomics platforms

•  TBD: Integrations with other visualization and analysis packages

INTEROPERABILITY  

INTEROPERABILITY

Genome Pattern CYTOSCAPE tranSMART I2B2

SYNAPSE  

CTCAP  The  Federa/on  Portable  Legal  Consent  Sage  Congress  Project  

Arch2POCM  

Five  Pilots  involving  Sage  Bionetworks  

RULES GOVERN

PLAT

FORM

NEW

MAP

S

Clinical Trial Comparator Arm Partnership (CTCAP)

  Description: Collate, Annotate, Curate and Host Clinical Trial Data with Genomic Information from the Comparator Arms of Industry and Foundation Sponsored Clinical Trials: Building a Site for Sharing Data and Models to evolve better Disease Maps.

  Public-Private Partnership of leading pharmaceutical companies, clinical trial groups and researchers.

  Neutral Conveners: Sage Bionetworks and Genetic Alliance [nonprofits].

  Initiative to share existing trial data (molecular and clinical) from non-proprietary comparator and placebo arms to create powerful new tool for drug development.

Started Sept 2010

Shared clinical/genomic data sharing and analysis will maximize clinical impact and enable discovery

•  Graphic  of  curated  to  qced  to  models  

The  Federa/on  

2008   2009   2010   2011  

How can we accelerate the pace of scientific discovery?

Ways to move beyond “traditional” collaborations?

Intra-lab vs Inter-lab Communication

Colrain/ Industrial PPPs Academic Unions

(Nolan  and  Haussler)  

sage federation: model of biological age

Faster Aging

Slower Aging

Clinical Association -  Gender -  BMI -  Disease Genotype Association Gene Pathway Expression Pr

edicted  Age  (liver  expression)  

Chronological  Age  (years)  

Age Differential

Reproducible  science==shareable  science  

Sweave: combines programmatic analysis with narrative

Sweave.Friedrich Leisch. Sweave: Dynamic generation of statistical reports using literate data analysis. In Wolfgang Härdle and Bernd Rönz,editors, Compstat 2002 –

Proceedings in Computational Statistics,pages 575-580. Physica Verlag, Heidelberg, 2002. ISBN 3-7908-1517-9

Dynamic generation of statistical reports using literate data analysis

TP53 mut

CDKN2A copy

MDM2 expr

HGF expr

CML linage EGFR mut

EGFR mut

EGFR mut

CML lineage

ERBB2 expr

BRAF mut

BRAF mut

NRAS mut

BRAF mut

NRAS mut

KRAS mut

BRAF mut

NRAS mut

KRAS mut

#1  BRAF  mut  

#2  NRAS  mut  #1  BRAF  mut  

#3  KRAS  mut  #2  NRAS  mut  #1  BRAF  mut  

#3  KRAS  mut  #2  NRAS  mut  #1  BRAF  mut  

#1  EGFR  mut  

#1  ERBB2  expr  

#1  EGFR  mut  

#2  CML  lineage  #1  EGFR  mut  

#1  CML  lineage  

#1  HGF  expr  

#2  TP53  mut  #3  CDKN2A  copy  #1  MDM2  expr  

Can  the  approach  make  new  discoveries?  

For 11/12 compounds, the #1 predictive feature in an unbiased analysis corresponds to the known stratifier of sensitivity

35  

Vaske,  et  al.  

Presentation outline

Currently   mRNA   copy number   somatic mutations (36

cancer-related genes) In progress   targeted exon sequencing   epigenetics   microRNA   lncRNA   phospho-tyrosine kinase   metabolites

Molecular characterization (1,000 cell lines)

Viability screens (500 cell lines, 24 compounds)

Small molecule screen

Cancer  cell  line  encyclopedia  

TCGA  /ICGC  Molecular characterization (50 tumor types)

  genomics   transcriptomics   epigenetics

Clinical data Predic&ve  model  

1)  Predic&ng  drug  response  from  cancer  cell  lines  

2)  Future  approaches:  network-­‐based  predictors  and  mul&-­‐task  learning  

3)  Standardized  workflows  for  data  management,  versioning  and  method  comparison  

Transfer  learning  

Network  /  pathway  prior  informa&on  

Vaske,  et  al.  

1)  Data  management  APIs  to  load  standaridzed  objects,  e.g.  R  ExpressionSets  (MaP  Furia):  

         ccleFeatureData  <-­‐  getEn/ty(ccleFeatureDataId)            ccleResponseData  <-­‐  getEn/ty(ccleResponseDataId)  

         tcgaFeatureData  <-­‐  getEn/ty(tcgaFeatureDataId)            tcgaResponseData  <-­‐  getEn/ty(tcgaResponseDataId)  

=!

Observed Data!=! +!

+!

Random Variation!Systematic Variation!

+!

Normalization: Remove the influence of adjustment variables on data...!

=! +!

2)    Automated,  standardized  workflows  for  cura&on  and  QC  of  large-­‐scale  datasets  (Brig  Mecham).  

A.  TCGA:  Automated  cloud-­‐based  processing.  B. GEO  /  Array  Expression:  Normaliza/on  workflows,  cura/on  of  phenotype  using  standard  ontologies.  C. Addi/onal  studies  with  gene/c  and  phenotypic  data  in  Sage  repository  (e.g.  CCLE  and  Sanger  cell  line  datasets)  

custom  model  1   custom  model  2   custom  model  N  

4)  Sta&s&cal  performance  assessment  across  models.  

custom  model  1   custom  model  2   custom  model  N  

5)  Output  of  candidate  biomarkers  and  feature  evalua&on  (e.g.  GSEA,  pathway  analysis)  

6)  Experimental  follow-­‐up  on  top  predic&ons  (TBD)        E.g.  for  cell  lines:  medium  throughput  suppressor  /  enhancer  screens  of  drug  sensi/vity  for  knockdown  /  overexpression  of  predicted  biomarkers.  

3)  Pluggable  API  to  implement  predic&ve  modeling  algorithms.  

A)  Support  for  all  commonly  used  machine  learning  methods  (for  automated  benchmarking  against  new  methods)  

B)  Pluggable  custom  methods  as  R  classes  implemen/ng  customTrain()  and  customPredict()  methods.  

A)  Can  be  arbitrarily  complex  (e.g.  pathway  and  other  priors)  

B)  Support  for  paralleliza/on  in  for  each  loops.  

Portable  Legal  Consent  

(Ac/va/ng  Pa/ents)  

John  Wilbanks  

Sage  Congress  Project  April  20  2012  

RealNames  Parkinson’s  Project  Revisi/ng  Breast  Cancer  Prognosis  

Fanconi’s  Anemia  

(Responders  Compe//ons-­‐  IBM-­‐DREAM)  

Confidential | © 2012 Third Rock Ventures

THE QUICK WIN, FAST FAIL DRUG DEVELOPMENT PARADIGM

March 1, 2012 PAGE 40

Preclinical development Phase I

Phase II

Test each scarce molecule thoroughly

Phase III Scarcity of drug discovery

Abundance of drug discovery

CS FHD FED PD Launch

PD Launch

•  Increase critical information content early to shift attrition to cheaper phase

•  Use savings from shifted attrition to re-invest in the R&D ‘sweet spot’

FHD

POC

CS

Preclinical development

Confirmation, dose finding Commercialization

R&D ‘sweet spot’

TRADITIONAL

QUICK WIN, FAST FAIL

Higher p(TS)

$ $ $$ $$$$

Source: Nature Publishing Group

Arch2POCM  

Restructuring  the  Precompe//ve  Space  for  Drug  Discovery  

How  to  poten/ally  De-­‐Risk      High-­‐Risk  Therapeu/c  Areas  

Arch2POCM: Highlights A PPP To De-Risk Novel Targets That The Pharmaceutical Industry Can

Then Use To Accelerate The Development of New and Effective Medicines •  The Arch2POCM will be a charitable Public Private Partnership (PPP) that will file no patents and

whose scientific plan (including target selection) will be endorsed by its pharmaceutical, private and public funders

•  Arch2POCM will de-risk novel targets by developing and using pairs of test compounds (two different chemotypes) that interact with the selected targets: the compounds will be developed through Phase IIb clinical trials to determine if the selected target plays a role in the biology of human disease

•  Arch2POCM will work with and leverage patient groups and clinical CROs to enable patient recruitment, and with regulators to design novel studies and to validate novel biomarkers

•  Arch2POCM will make its GMP test compounds available to academic groups and foundations so they can use them to perform clinical studies and publish on a multitude of additional indications

•  Arch2POCM will release all reagents and data to the public at pre-defined stages in its drug development process. To ensure scientific quality, data and reagents will be released once they have been vetted by an independent scientific committee

•  Arch2POCM will publish all negative POCM data immediately in order to reduce the number of ongoing redundant proprietary studies (in pharma, biotech and academia) on an invalidated target and thereby –  minimize unnecessary patient exposure –  provide significant economic savings for the pharmaceutical industry

•  In the rare instance in which a molecule achieves positive POCM, Arch2POCM will ensure that the compound has the ability to reach the market by arranging for exclusive access to the proprietary IND database for the molecule 42

Arch2POCM: scale and scope

•  Proposed Goal: Initiate 2 programs. One for Oncology/Epigenetics/Immunology. One for Neuroscience/Schizophrenia/Autism. Both programs will have 6-8 drug discovery projects (targets) - ramped up over a period of 2 years

–  It is envisioned that Arch2POCM’s funding partners will select targets that are judged as slightly too risky to be pursued at the top of pharma’s portfolio, but that have significant scientific potential that could benefit from Arch2POCM’s crowdsourcing effort

•  These will be executed over a period of 5 years making a total of 16 drug discovery projects

–  Projected pipeline attrition by Year 5 (assuming 12 targets loaded in early discovery)

•  30% will enter Phase 1 •  20% will deliver Ph 2 POCM data 43

Arch2POCM: proposed funding strategy

–  Arch2POCM funding will come from a combination of public funding from governments and private sector funding from pharmaceutical and biotechnology companies and from private philanthropists

–  By investing $1.6 M annually into one or both of Arch2POCM’s selected disease areas, partnered pharmaceutical companies:

1.  obtain a vote on Arch2POCM target selection 2.  gain real time data access to Arch2POCM’s12- 16 drug discovery

projects 3.  have the strategic opportunity to expand their overall portfolio

44

Lead identification Phase I Phase II Preclinical

Lead optimisation

Assay in vitro probe

Lead Clinical candidate

Phase I asset

Phase II asset

Pioneer targets - genomic/ genetic - disease networks - academic partners - private partners - SAGE, SGC,

Stage-gate 1: Early Discovery and PCC Compounds (75%)

Stage-gate 2: Pharma’s re-purposed clinical assets (25%) 45

Entry points for Arch2POCM programs: Two compounds (different chemotypes) will be advanced per target

Five Year Objective: Initiate ≈ 8 drug discovery projects with 6 entering in Early Discovery, one entering in pre-clinical and one entering in PH I

Months → 0-6 7-12 13-18 19-24 25-30 31-36 37-42 43-48 49-54 55-60

Pipeline flow for Arch2POCM

Early discovery (45% PTRS) Pre-clinical (70% PTRS) Ph I (65% PTRS)

Ph II (10% PTRS)

1.3

1

Ph 1 (1)

1

Year #2 Arch2POCM Target Load

Arch2POCM Snapshot at Year 5

Year #1 Arch2POCM Target Load

Early discovery (2)

1

Targets  Loaded   8  

Projected  INDs  filed   3-­‐4  

Ph  1  or  2  Trials  In  Progress   2  

Projected  Complete  Ph  2  (POCM)  Data  Sets  

1  

*PTRS = Probability of technical and regulatory success

Pre-clinical (1)

Early discovery (4)

Pre-clinical

Pre-clinical

Ph 1

Ph 1

Ph 1

Ph 2

Ph 2

Ph 2

46

The case for epigenetics/chromatin biology

1.  There are epigenetic oncology drugs on the market (HDACs)

2.  A growing number of links to oncology, notably many genetic links (i.e. fusion proteins, somatic mutations)

3.  A pioneer area: More than 400 targets amenable to small molecule intervention - most of which only recently shown to be “druggable”, and only a few of which are under active investigation

4.  Open access, early-stage science is developing quickly – significant collaborative efforts (e.g. SGC, NIH) to generate proteins, structures, assays and chemical starting points

47

Domain Family Typical substrate class* Total Targets

Histone Lysine demethylase

Histone/Protein K/R(me)n/ (meCpG) 30  

Bromodomain Histone/Protein K(ac) 57  

R O Y A L

Tudor domain Histone Kme2/3 - Rme2s 59  

Chromodomain Histone/Protein K(me)3 34  

MBT repeat Histone K(me)3 9  

PHD finger Histone K(me)n 97  

Acetyltransferase Histone/Protein K 17  

Methyltransferase Histone/Protein K&R 60  

PARP/ADPRT Histone/Protein R&E 17  

MACRO Histone/Protein (p)-ADPribose 15  

Histone deacetylases Histone/Protein KAc 11  

395  

The current epigenetics universe

Now known to be amenable to small molecule inhibition 48

Why is Arch2POCM a “smart bet” for Pharma investment?

Arch2POCM:  an  external  epigene/c  think  tank  from  which  Pharma  can  load  the  most  likely  to  succeed  targets  as  proprietary  programs  or  leverage  Arch2POCM  results  for  its  other  internal  efforts  •  A  front  row  seat  on  the  progression  of  6-­‐  8  epigene/c  targets  means  that:  

•  Pharma  can  select  the  epigene/c  targets  that  best  compliment  their  internal  pormolio  and  for  which  there  is  the  greatest  interest  

•  Pharma  can  structure  Arch2POCM’s  projects  so  that  key  objec/ves  line  up  with  internal  go/no-­‐go  decisions  

•  Pharma  can  use  Arch2POCM  data  to  trigger  its  internal  level  of  investment  on  a  par/cular  target  

•  Pharma  can  use  Arch2POCM  resources  to  enrich  their  internal  epigene/cs  effort:  ac/ve  chemotypes,  assays,  pre-­‐clinical  models,  biomarkers,  gene/c  and  phenotypic  data  for  pa/ent  stra/fica/on,  rela/onships  to  epigene/c  experts  

•   Pharma  can  use  Arch2POCM’s  lead  compound  chemotypes  to:  •   inform  their  proprietary  medicinal  chemistry  efforts  on  the  target  

•   iden/fy  chemical  scaffolds  that  impact  epigene/c  pathways:  a  proprietary  combina/on  therapy  opportunity  

•   Toxicity  screening  of  Arch2POCM  compounds  with  FDA  tools  can  be  used  to  guide  internal  proprietary  chemistry  efforts  in  oncology,  inflamma/on  and  beyond    

•  Arch2POCM’s  crowd  of  scien/sts  and  clinicians  provides  its  Pharma  partners  with  parallel  shots  on  goal  at  the  best  context  for  Arch2POCM’s  compounds/targets   49

How will Arch2POCM provide “line of sight” to new medicines?

Arch2POCM will partner with scientists, clinicians and CROs that:

•  use “Omics” approaches to construct predictive models of disease networks (genomic, proteomic, signaling and metabolic)

•  have strategies available to identify those disease network gene(s) which when perturbed, impact the overall functioning of the network

•  already have epigenetic assays in place to identify chemotype structures (from discovery and/or pharma’s re-purposed un-used clinical assets) that impact the target and disease-correlated molecular phenotypes

•  already have biomarker tools available that can be tested for correlation to Arch2POCM’s targets

•  already have access to patient data and/or patient groups to mine for genetic and phenotypic signatures that may represent best responders for Arch2POCM clinical trials

50

•  Arch2POCM’s Ph II validation of high risk high opportunity targets focuses Pharma’s NME efforts

•  Positive POCM data: De-risked validated targets for Pharma development •  Negative POCM data: public release of this data minimizes the amount of time

and money that Pharma and the industry place on failed targets

•  Arch2POCM’s clinical candidate compounds provide Pharma with multiple paths to new medicines

•  Arch2POCM compounds that achieve POCM can be advanced into Ph 3 by Arch2POCM Members

•  The purchaser of Arch2POCM’s IND database obtains a significant time advantage over competitors to generate Phase III data and proceed to market

•  NMEs that derive from Arch2POCM will launch with database exclusivity protections: 5-8 years to garner a return on investment

•  The crowd’s testing of Arch2POCM compounds may identify alternative/better contexts for agonizing/antagonizing the disease biology target

•  indications •  patient stratification •  combination therapy options

How will Arch2POCM provide “line of sight” to new medicines?

51

Arch2POCM: current partnering status •  Pharmaceutical Funding Partners

–  Three companies are considering a potential role as industry anchors for Arch2POCM –  Two companies have demonstrated interest in Arch2POCM and their company leadership wants to

go to next step- awaiting face to face discussions to go over agreement

•  Public Funding Partners

–  Good progress is being made to obtain financial backing for Arch2POCM from public funders in a number of countries (Canada, United Kingdom and Sweden) for both epigenetics and for CNS

–  Ontario Brain Institute, Canada has allocated $3M to the development of an autism clinical network that is committed to work with Arch2POCM

•  Philanthropic Funding Partners: awaiting designation of anchor partners

•  In kind partners –  GE Healthcare (imaging): lead diagnostics partner and willing to share its experimental oncology

biomarkers –  Cancer Research UK: through some of its drug discovery and development resources considering

participating in Arch2POCM through “in kind efforts” •  Academic partners

–  Institutions that have indicated willingness to let their scientists participate without patent filing: UCSF, Massachusetts General Hospital, University of North Carolina, University of Toronto, Oxford University, Karolinska Institute

–  Academic community of epigenetic experts/resources already identified

•  Regulatory partners: Because the objective of the Arch2POCM PPP is to probe and elucidate disease biology as opposed to develop new proprietary products, FDA and EMEA are ready to play an active role (toxicity screens, and legacy clinical trial data)

•  Patient group partners: leaders from Genetic Alliance, Inspire2Live and the Love Avon Army of Women are actively engaged 52

Confidential | © 2012 Third Rock Ventures

STRATEGIC INFLECTION: FORCES AFFECTING A BUSINESS

MDAndersonCC02272012 PAGE 53

Society’s Needs Customers

Suppliers

New Technologies

New Competitors

Businesses Academia Government

Networking  Disease  Model  Building